Many previous works [1]}, [2]}, [3]} have observed that catastrophic forgetting in neural networks occurs during domain transferring, and the degree of forgetting is related to the gap between the source domain and the target domain. To evaluate the domain gap between the pre-training data and the target domain, a simple metric named Catastrophic Forgetting Score (CFS) is proposed as below, which computes the representation similarity between \(\theta _s(x_s^i)\)  and \(\theta _t(x_s^i)\)  for the pre-training data \(x_s^i\) :
\(c_s^i = \frac{\langle \theta _s(x_s^i), \theta _t(x_s^i)\rangle }{||\theta _s(x_s^i)||\, ||\theta _t(x_s^i)||}.\) 
